Faculty, Staff and Student Publications
Publication Date
7-18-2024
Journal
Human Genetics and Genomic Advances
Abstract
Approximately 20% of breast cancer cases are attributed to increased family risk, yet variation in BRCA1/2 can only explain 20%-25% of cases. Historically, only single gene or single variant testing were common in at-risk family members, and further sequencing studies were rarely offered after negative results. In this study, we applied an efficient and inexpensive targeted sequencing approach to provide molecular diagnoses in 245 human samples representing 134 BRCA mutation-negative (BRCAX) hereditary breast and ovarian cancer (HBOC) families recruited from 1973 to 2019 by Dr. Henry Lynch. Sequencing identified 391 variants, which were functionally annotated and ranked based on their predicted clinical impact. Known pathogenic CHEK2 breast cancer variants were identified in five BRCAX families in this study. While BRCAX was an inclusion criterion for this study, we still identified a pathogenic BRCA2 variant (p.Met192ValfsTer13) in one family. A portion of BRCAX families could be explained by other hereditary cancer syndromes that increase HBOC risk: Li-Fraumeni syndrome (gene: TP53) and Lynch syndrome (gene: MSH6). Interestingly, many families carried additional variants of undetermined significance (VOUSs) that may further modify phenotypes of syndromic family members. Ten families carried more than one potential VOUS, suggesting the presence of complex multi-variant families. Overall, nine BRCAX HBOC families in our study may be explained by known likely pathogenic/pathogenic variants, and six families carried potential VOUSs, which require further functional testing. To address this, we developed a functional assay where we successfully re-classified one family's PMS2 VOUS as benign.
Keywords
Cancer, Hereditary cancer, Lynch Syndrome, Hereditary breast and ovarian cancer (HBOC) syndrome, Targeted sequencing, Variants of undetermined significance
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Medical Sciences Commons, Oncology Commons
Comments
Supplementary Materials
PMID: 38734904